Individual Multimodal Pathway Statistics for Predicting Treatment Response in Late-life Depression

用于预测晚年抑郁症治疗反应的个体多模式通路统计

• 批准号: 10722921
• 负责人: Andrew Robert Gerlach
• 金额: $ 17.58万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-03 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10722921
• 关键词: Affect; Age; Algorithms; Antidepressive Agents; Anxiety Disorders; Biological Markers; Brain; Clinical; Cognitive; Data; Data Collection; Depressed mood; Development; Diffusion Magnetic Resonance Imaging; Disease remission; Doctor of Philosophy; Elderly; Engineering; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Health; Image; Impairment; Individual; K-Series Research Career Programs; Knowledge; Magnetic Resonance Imaging; Measurement; Measures; Mediating; Mediator; Mental Depression; Methods; Modality; Modeling; Montgomery and Asberg depression rating scale; Mood Disorders; National Institute of Mental Health; Nature; Neurobiology; Nuclear; Outcome; Participant; Pathway interactions; Performance; Physics; Play; Positioning Attribute; Prediction of Response to Therapy; Prognosis; Psychiatry; Radiology Specialty; Reproducibility; Research; Research Design; Research Personnel; Resolution; Rest; Risk; Role; Scanning; Scheme; Science; Scientist; Severities; Specificity; Structure; Testing; Training; Treatment Protocols; Treatment outcome; Universities; career; clinical predictors; clinical translation; collaborative environment; computational neuroscience; computer framework; depressive symptoms; executive function; geriatric depression; gray matter; high dimensionality; human subject; image processing; image registration; improved; interest; late-life anxiety; machine learning model; multimodal data; multimodal neuroimaging; multimodality; neural circuit; neurobiological mechanism; overtreatment; predictive modeling; primary outcome; recruit; response; statistics; tool; treatment response; white matter; white matter change

Modest response rates to first-line antidepressant treatment for late-life depression (LLD) expose individuals to prolonged depressive symptoms that worsen their prognosis and associated health risks. Biomarkers of treatment response can alleviate this burden by identifying individuals most likely to benefit from antidepressant treatment. MRI measures of brain structure and function are a promising tool to identify such biomarkers, though the performance required for clinical translation has remained elusive. The goal of this proposal is to integrate complementary network measures from structural and functional MRI with clinical measures to generate biologically relevant features that can improve prediction of treatment outcome in LLD. The anticipated impact of this research will provide improved personalization of LLD treatment (NIMH Strategic Objective 3.2), while elucidating the neural circuitry indicative of treatment outcome (Objective 1.3). To achieve this goal, structural, resting state, and diffusion-weighted MRI will be collected from 75 participants with LLD before commencing an algorithmic antidepressant treatment protocol. The role of resting state functional connectivity as a mediator of the relationship between structural connectivity and clinical measures (baseline depression severity and change in depression severity over treatment) will be investigated within key neural circuitry at the group level. Individual Multimodal Pathway Statistics (IMPathS) will be derived to quantify the personalized importance of functional connectivity to the relationship between structural connectivity and depression severity for prediction of treatment outcome at the individual level. Utility of IMPathS will be assessed by their ability to improve performance beyond unimodal MRI and clinical predictors. Dr. Gerlach has a PhD in nuclear engineering and radiological sciences and is completing a transition from computational physics to computational neuroscience. He will require additional training in 1) the neurobiology, clinical manifestations, and treatment of LLD, 2) diffusion-weighted imaging processing and analysis, 3) advanced statistical training for development and testing of IMPathS, 4) human subjects, study design, and data collection. Completion of the training and research plan in this career development award will enable Dr. Gerlach to progress to an independent investigator focused on investigating the neurobiology of late life anxiety and mood disorders through improved integration of multimodal neuroimaging measures. Dr. Gerlach will execute this training and research with the full support of the Department of Psychiatry at the University of Pittsburgh, which is a highly collaborative environment focused on the development of early career scientists.

对晚期抑郁症（LLD）一线抗抑郁治疗的适度应答率使个体暴露于 长期的抑郁症状会恶化他们的预后和相关的健康风险。的生物标志物 治疗反应可以通过识别最有可能受益于 抗抑郁治疗脑结构和功能的MRI测量是一种有前途的工具，以确定这种 生物标志物，虽然临床翻译所需的性能仍然难以捉摸。这个目标 建议将结构和功能MRI的互补网络措施与 产生生物学相关特征的临床措施，可改善治疗预测 LLD的结果。这项研究的预期影响将提供更好的个性化LLD 治疗（NIMH战略目标3.2），同时阐明指示治疗结果的神经回路 （目标1.3）。为了实现这一目标，将收集以下患者的结构、静息状态和弥散加权MRI： 75名参与者在开始算法抗抑郁治疗方案之前患有LLD。的作用 静息状态功能连接作为结构连接和 临床指标（基线抑郁严重程度和治疗期间抑郁严重程度的变化）将 在群体水平上对关键神经回路进行了研究。个体多模式路径统计（IMPathS） 将得出量化的功能连接之间的关系的个性化的重要性 结构连接性和抑郁严重程度，用于预测个体水平的治疗结果。实用 IMPathS将通过其改善单峰MRI和临床性能的能力进行评估 预测器盖拉赫博士拥有核工程和放射科学博士学位， 从计算物理学到计算神经科学的转变。他将需要额外的培训1） LLD的神经生物学、临床表现和治疗，2）弥散加权成像处理， 分析，3）IMPathS开发和测试的高级统计培训，4）人类受试者，研究 设计和数据收集。完成本职业发展奖的培训和研究计划将 使盖拉赫博士能够成为一名独立的研究人员，专注于研究最近的神经生物学。 生活焦虑和情绪障碍，通过改善多模态神经影像措施的整合。博士 盖拉赫将在精神病学系的全力支持下执行这项培训和研究。 匹兹堡大学，这是一个高度协作的环境，专注于早期的发展， 职业科学家